Language network lateralization is reflected throughout the macroscale functional organization of cortex

Hemispheric specialization is a fundamental feature of human brain organization. However, it is not yet clear to what extent the lateralization of specific cognitive processes may be evident throughout the broad functional architecture of cortex. While the majority of people exhibit left-hemispheric language dominance, a substantial minority of the population shows reverse lateralization. Using twin and family data from the Human Connectome Project, we provide evidence that atypical language dominance is associated with global shifts in cortical organization. Individuals with atypical language organization exhibit corresponding hemispheric differences in the macroscale functional gradients that situate discrete large-scale networks along a continuous spectrum, extending from unimodal through association territories. Analyses reveal that both language lateralization and gradient asymmetries are, in part, driven by genetic factors. These findings pave the way for a deeper understanding of the origins and relationships linking population-level variability in hemispheric specialization and global properties of cortical organization.

Secondly, if I understand the methods description correctly, 3 of the 5 features that are used for the asymmetry phenotype classification are derived from the HCP rs-fMRI, while the same rs-fMRI was utilized to derive the 3 gradient asymmetries. That means that the rs-fMRI data contributed (although with different processing steps and to different degree) both to determining the independent and dependent variable used in the second set of analyses (presented under "Gradients asymmetries and atypical lateralization"). Consequently, it is not clear whether the found "gradient" differences between the phenotype groups, simply reflect that the groups were determined (partly) by the same data. Thus, the authors need to convince that this is not the case.
Finally, I feel the manuscript is densely written and could benefit from thorough language editing. Many sentences are (at least in my opinion) unnecessarily complicated and make it difficult to understand what the authors mean. One of several examples: "The parallel and interdigitated organization of cortical networks suggests that the language system may impinge upon, and be influenced by, putatively distinct yet spatially adjacent networks" (top of 2nd page of discussion). As nice as this sounds, does this not just say that language processing relies on cortical networks that may interact (which is not surprising).
Reviewer #2 (Remarks to the Author): The study uses data from the Human Connectome Project (HCP) to quantify hemispheric language dominance based on task fMRI, and relate it to hemispheric differences in macroscale functional gradients based on resting fMRI. In addition, the study shows heritability of language laterality and gradient asymmetries using HCP twin data. The findings contribute to understanding how specific task-functional organization can relate to broader aspects of brain network organization. The findings might also provide a route to using large-scale resting fMRI data to perform molecular genetic studies of hemispheric language dominance. I find the study clearly described and a useful contribution to the field. Further consideration could be given to the following issues: -The variation across individuals is quantitative and continuous, but the authors rely on clustering to create groups (categories) for subsequent analyses. The particular clusters that were defined do not appear especially robust compared to alternative cluster solutions. The issue could be avoided by treating the data as continuous. If not, then a stronger rationale could be given for the clusterbased approach, together with clearer indications that the chosen solution was robust compared to others.
-Hemispheric language dominance is most pronounced for language production tasks. The particular task contrast used in HCP involves comparing comprehension of brief narratives with an arithmetic task. The Discussion would benefit from some consideration of this issue. Was the HCP task optimal for determining hemispheric language dominance? -The study reports heritability of lateralization of the language network, and also heritability of hemispheric asymmetries in gradient organization. The same data and software could be used to assess the genetic correlation between these two types of measure, i.e. to what extent do the same genetic variants affect hemispheric language dominance and asymmetries of gradient organization, versus being independently heritable (and therefore correlated for non-genetic reasons).
-For introducing/discussing the genetic parts it would be relevant to cite recent genetic association studies of left-handedness and brain asymmetry: https://www.nature.com/articles/s41562-020-00956-y https://www.nature.com/articles/s41562-021-01069-w https://www.pnas.org/doi/10.1073/pnas.2113095118 https://www.nature.com/articles/s41598-019-42515-0 https://academic.oup.com/brain/article/142/10/2938/5556832 Reviewer #3 (Remarks to the Author): Leveraging a higher-order language atlas and following previous work, the authors performed a hierarchical clustering based on a combination of resting-state and task-evoked fMRI features. They identified 3 subgroups of subjects related to language lateralization in the HCP database. Gradients were computed through diffusion map embedding on functional connectivities among the AICHA atlas ROIs. Asymmetry of each gradient in typical subjects was calculated as the difference between gradient values in the left and right hemispheres for each canonical network. Then, the impact of language lateralization on the asymmetry of each canonical network was assessed using an ANCOVA, comparing atypical subjects for language lateralization and typical and mild typical subjects merged into a single group. Finally, using a multidimensional heritability analysis, genetic contribution to the phenotypic variance of language network lateralization, on the one hand, and the hemispheric asymmetries in gradient organization, on the other hand, was assessed. In addition, the heritability of gradient asymmetry values for each canonical network was estimated.
The manuscript is well-written, and the subject is highly interesting and original. Many analyses have been carried out, using various methods and uncovering valuable results. However, I would have some questions about several points.
The experimental design of this work is very complex, and for the sake of clarity, the author could provide a diagram of the relationships between the different analyses and results.
When identifying subgroups of subjects based on their language network lateralization using hierarchical clustering, the number of subgroups was set to 3, based on previous work (Labache et al. Elife 2020). In this previous work, the cohort was enriched for left-handed subjects with no twin pairs or siblings, which is not the case in HCP. Moreover, input features based on task-fMRI data were derived from production, reading and listening tasks versus a Story-Math contrast in the current study. I wonder if the 3-subgroup result in Labache et al. Elife 2020 could be directly applied here, without any machine learning optimization of this hyperparameter, considering that the input is different? It would be valuable to reproduce this result in similar but not identical conditions.
Multiple test correction is not mentioned in the ANCOVA of language lateralization and the 5 input features of hierarchical clustering. Are the reported p-values corrected or not? In supplementary tables 10 and 11, when correcting for multiple testing, the correction accounted for 7 networks, but there were also 3 gradients for each network; I wonder if the p-values should have been corrected for 21 tests instead of only 7, as in supplementary figure 2? Same question for ANCOVA of language lateralization and lateralization of large-scale cortical organization.
The authors conclude that genetic factors substantially influence the lateralization of both specific cognitive functions (language, I suppose) and the broad functional organization of the cortex. As covariance between language lateralization and canonical networks lateralization was assessed, the genetic correlation between these could also be computed to give even more insight into the genetic architecture of brain lateralization.
The supplementary figure 2 is never commented on.
For clarity, the third paragraph of "Gradients asymmetries atypical lateralization" should refer to Supplementary table 10. Typo at the end of paragraph : dorsal-attentional (mu_typical=-0.61…) " dorsal-attentional (mu_L-R(typical)=-0.61…) I would recommend this work for publication in Nature Communications with major revisions.